Monitoring crop conditions is important for the economic development of any nation, particularly developing ones. The use of remote sensing has proved to be very important in monitoring the growth of agricultural crops and irrigation scheduling. Efforts have been made to develop various indices for different crops for different regions worldwide. Generally, crop production has a direct impact on year-to-year variations on national and international economies and food supply.
Weather and climatic conditions are important parameters in controlling the yield of agricultural crops. Crop-weather relations have been used for predicting crop yield. Least-square regression is a principle method usually applied into crop-weather relationships, which may be complex and non-linear. However, conventional approaches of forecasting crop yield using ground based data collection is tedious, time consuming and often difficult.
Using remote sensing data, efforts have been made to develop various indices such as vegetation condition index (VCI), thermal condition index (TCI) and normalized difference vegetation index (NDVI). VCI and TCI are well known for drought detection, monitoring excessive soil wetness, assessment of weather impacts on vegetation and evaluation of vegetation health and productivity. NDVI reflects vegetation greenness; it indicates levels of healthiness in the vegetation development. Though vegetation development of crop fields may differ from those of natural vegetation because of human influences involved such as irrigation, fertilization and pesticidal activity, NDVI can be a valuable source of information for the crop conditions. NDVI data may be used extensively in vegetation monitoring, crop yield assessment and forecasting.
In recent years, remote sensing data is being extensively used for monitoring the state of the agricultural fields, vegetation cover and also to estimate crop yield in different regions around the globe. Yet, most existing crop yield models require various types of field data, which are not always or accurately available. Overall, field data can affect the quality of yield estimates. Different methods have been developed to estimate crop yields by means of satellite data. These include a neural network, autoregressive (AR) state-space models, least-square regression, exponential-linear (EL) crop growth algorithm and numerical crop yield model. Each has been used to predict crop yield with moderate success. Among numerous crop studies, many utilized vegetation indices derived from remotely sensed visible and near-infrared reflection data sets provided by Landsat Thematic Mapper (TM), Systeme Probatoire d'Observation de la Terra (SPOT) or National Oceanic and Atmospheric Administration's (NOAA) Advanced Very High Resolution Radiometer (AVHRR). Earlier studies, in general, utilized NDVI value alone, but included transformations such as simple integrations of NDVI to more complicated creations of a new vegetation index based on NDVI. For example, NDVI integration during the wheat grain filling period was used on a simple linear regression model to derive wheat yield prediction for Emilia Romagna, Italy from 1986 to 1989, which resulted 2.3% to 18.9% of relative difference with the official yield estimates. A similar approach of a simple model using a cumulative NDVI was studied for cotton, maize, rice and wheat in northwest Thessaloniki, northern Greece from 1987 to 1989, showing 0% to 43% of difference in the yield prediction from the official data. In addition, a standardized NDVI created by multiannual NDVI statistics was used in a linear regression model for millet and sorghum yield prediction for Niger in Sahelian region from 1986 to 1989, of which the prediction showed R=0.85 with the estimate of Food and Agricultural Organization (FAO). Furthermore, VCI was used in a quadratic model to predict maize crop yield for 42 Crop Reporting Districts (CRDs) of the United States Corn Belt from 1985 to 1992, which showed 0.1% to 10.4% of departure from the United States Department of Agriculture (USDA) final estimates.
In addition to using NDVI in crop yield models, efforts have also been made to use other variables such as Leaf Area Index (LAI), albedo and meteorological data (precipitation and temperature as well as land use classifications). For example, more than 40 of biogeochemical and meteorological parameters were used in a general large area model (GLAM) to predict groundnut yields of India, which has shown the correlation coefficient 0.76 between observed and simulated yields. Moreover, NDVI field classification was found to be a major advantage for CROPGRO-Soybean model, and CERES-Maize model was used to find the model explained 76% of the yield variability of a relatively small area (20-ha field) in central Iowa for 3 years (Batchelor, 2002). Multivariables, such as reflectance data from satellite observation, land use maps, sowing dates and leaf area index of a specific crop (e.g., winter wheat), have also been used for a crop growth model. Because the inter-annual variability of crop production rate has been a general concern of the crop yield prediction models, in a spatial variability, NDVI has been strongly suggested to be a major parameter of the models, showing higher correlation with the yields than potential extractable soil water for a soybean model.
Currently, the most common approach is to develop empirical relationships between NDVI and crop yield. However, the general drawback of most methods using statistical relationships between NDVI and crop yield is that they have a strong empirical character and that the correlation coefficients are moderate to low. To overcome this problem, a reasonable approach is needed to incorporate heterogeneity in inputs over the production environment to minimize sources of variability and errors in yield prediction. In spite of the availability of numerous crop-growth models, one main difficulty still arises due to crop model input requirements: climate can be a major factor affecting crop yield. Therefore, what is needed is a method that better predicts crop yield by incorporating multiple vegetation and environmental factors, such as NDVI, soil moisture (SM), surface temperature (ST) and rainfall (Rainfall).